Since users may intend to search for different information by using similar keywords, it is crucial for search engines to understand actual user intent behind user search queries. Some search engines may rely on query discovery to classify queries according to semantic topics. Other more sophisticated search engines may classify queries into domains of query tasks that are to be accomplished. For example, when a user inputs the search query for “Xbox 360 purchase”, a search engine in the latter category may classify the search query into a “buy” task domain, and the results returned by the search engine may include information related to how to purchase an Xbox 360 game console. Additionally, advertisements delivered by the search engine may relate to electronic retailers that sell game consoles.
However, if the “buy” domain is absent when the search engine in the latter category executes the search query “Xbox 360 purchase”, the search engine may instead return results only generally related to the term “Xbox 360”, instead of results specifically focused on how to purchase an Xbox 360 game console. The development of domains and a task classifier specific to each of the domains for a search engine may involve considerable efforts in training data selection, feature selection, as well as model training. For example, a team of several developers may take as long as a week to develop of a single query task domain using such efforts. Thus, considering that a search engine may use thousands, if not tens or hundreds of thousands of query task domains to provide the most accurate search results, conventional approaches to query task domain development may become impractical.